Abstract: In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine ). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.

Abstract: One of the key points to take into account to foster effective introduction of AI planning and scheduling systems in real world is to develop end user trust in the related technologies. Automated planning and scheduling systems often brings solutions to the users which are neither “obvious� nor immediately acceptable for them. This is due to the ability of these tools to take into account quite an amount of temporal and causal constraints and to employ resolution processes often designed to optimize the solution with respect to non trivial evaluation functions. To increase technology trust, the study of tools for verifying and validating plans and schedules produced by AI systems might be instrumental. In general, validation and verification techniques represent a needed complementary technology in developing domain independent architectures for automated problem solving. This paper presents a preliminary report of the issues concerned with the use of two software tools for formal verification of finite state systems to the validation of the solutions produced by MrSPOCK, a recent effort for building a timeline based planning tool in an ESA project.

Abstract: The main barrier to automatic verification of concurrent systems is the huge amount of memory required to complete the verification task (state explosion). In this paper we present a probabilistic algorithm for automatic verification via model checking. Our algorithm trades space with time. In particular, when memory is full because of state explosion our algorithm does not give up verification. Instead it just proceeds at a lower speed and its results will only hold with some arbitrarily small error probability. Our preliminary experimental results show that by using our probabilistic algorithm we can typically save more than 30% of RAM with an average time penalty of about 100% w.r.t. a deterministic state space exploration with enough memory to complete the verification task. This is better than giving up the verification task because of lack of memory.

Abstract: In this paper we show that statistical properties of the transition graph of a system to be verified can be exploited to improve memory or time performances of verification algorithms. We show experimentally that protocols exhibit transition locality. That is, with respect to levels of a breadth-first state space exploration, state transitions tend to be between states belonging to close levels of the transition graph. We support our claim by measuring transition locality for the set of protocols included in the Mur$\varphi$ verifier distribution. We present a cache-based verification algorithm that exploits transition locality to decrease memory usage and a disk-based verification algorithm that exploits transition locality to decrease disk read accesses, thus reducing the time overhead due to disk usage. Both algorithms have been implemented within the Mur$\varphi$ verifier. Our experimental results show that our cache-based algorithm can typically save more than 40% of memory with an average time penalty of about 50% when using (Mur$\varphi$) bit compression and 100% when using bit compression and hash compaction, whereas our disk-based verification algorithm is typically more than ten times faster than a previously proposed disk-based verification algorithm and, even when using 10% of the memory needed to complete verification, it is only between 40 and 530% (300% on average) slower than (RAM) Mur$\varphi$ with enough memory to complete the verification task at hand. Using just 300 MB of memory our disk-based Mur$\varphi$ was able to complete verification of a protocol with about $10^9$ reachable states. This would require more than 5 GB of memory using standard Mur$\varphi$.

Abstract: The paper describes two complementary and integrable approaches, a probabilistic one and a deterministic one, based on classic and advanced modelling techniques for safety analysis of complex computer based systems. The probabilistic approach is based on classical and innovative probabilistic analysis methods. The deterministic approach is based on formal verification methods. Such approaches are applied to the gas turbine control system of ICARO co generative plant, in operation at ENEA CR Casaccia. The main difference between the two approaches, behind the underlining different theories, is that the probabilistic one addresses the control system by itself, as the set of sensors, processing units and actuators, while the deterministic one also includes the behaviour of the equipment under control which interacts with the control system. The final aim of the research, documented in this paper, is to explore an innovative method which put the probabilistic and deterministic approaches in a strong relation to overcome the drawbacks of their isolated, selective and fragmented use which can lead to inconsistencies in the evaluation results.